svmtrain
This function trains an SVM model based on known labels and their corresponding data which comprise an instance matrtix.
libsvm_options : A string of testing options in the same format as that of LIBSVM.
libsvm_options :
-s : svm_type; set type of SVM (default 0)
| 0 | C-SVC (multi-class classification) | |
| 1 | nu-SVC (multi-class classification) | |
| 2 | one-class SVM | |
| 3 | epsilon-SVR (regression) | |
| 4 | nu-SVR (regression) |
-t : kernel_type; set type of kernel function (default 2)
| 0 | linear: u’*v | |
| 1 | polynomial: | |
| 2 | radial basis function: | |
| 3 | sigmoid: | |
| 4 | precomputed kernel (kernel values in training_instance_matrix) |
-d : degree; set degree in kernel function (default 3)
-g : gamma; set gamma in kernel function (default 1/num_features)
-r : coef0; set coef0 in kernel function (default 0)
-c : cost; set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n : nu; set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p : epsilon; set the epsilon in loss function of epsilon-SVR (default 0.1)
-m : cachesize; set cache memory size in MB (default 100)
-e : epsilon; set tolerance of termination criterion (default 0.001)
-h : shrinking; whether to use the shrinking heuristics, 0 or 1 (default 1)
-b : probability_estimates; whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-w : weight; set the parameter C of class i to weight*C, for C-SVC (default 1)
-v : n; n-fold cross validation mode
-q : quiet mode (no outputs)
The function svmtrain function returns a model structure which can be used for future prediction and it contains the following fields:
Parameters : parameters
nr_class : number of classes; = 2 for regression/one-class svm
totalSV : total #SV
rho : of the decision function(s)
Label : label of each class; empty for regression/one-class SVM
sv_indices : values in [1,...,num_traning_data] to indicate SVs in the training set
ProbA : pairwise probability information; empty if -b 0 or in one-class SVM
ProbB : pairwise probability information; empty if -b 0 or in one-class SVM
nSV : number of SVs for each class; empty for regression/one-class SVM
sv_coef : coefficients for SVs in decision functions
SVs : support vectors
If you do not use the option -b 1, ProbA and ProbB are empty matrices. If the ’-v’ option is specified, cross validation is conducted and the returned model is just a scalar: cross-validation accuracy for classification and mean-squared error for regression.
Source Code: svmtrain